Relational vs. non-relational databases: Key differences
Airtable
8 min read
Choosing the right database architecture is one of the most important decisions you'll make when building applications or managing data at scale. Today, there are many database systems to select from, though most fall into one of two categories: relational or non-relational. Understanding the differences between these two types of database helps you match your data storage strategy to your specific use case.
What’s the difference between relational and non-relational databases?
Relational databases organize structured data into tables with predefined schemas and use SQL queries to establish relationships between tables through foreign keys and primary key constraints. Non-relational databases (also called NoSQL databases) store data in flexible formats like documents, key-value pairs, or graph structures, allowing for schema-less data management and horizontal scaling across multiple nodes (data entities) to handle large volumes of unstructured or semi-structured data.
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What are relational databases?
A relational database is a type of database that organizes information into tables with rows and columns, where each table represents a specific entity. Relationships between tables are established through shared keys to reveal connections between related data points. Common relational database management system (RDBMS) platforms include MySQL, PostgreSQL, Oracle, and SQL Server.
The relational model relies on a predefined schema that enforces data structure and maintains data integrity through ACID properties (atomicity, consistency, isolation, and durability). This approach ensures data accuracy and consistency, making relational databases ideal for applications that require complex queries and strict data validation.
How do relational databases store data?
Relational databases store data in tabular format, organizing information into rows and columns within multiple tables. Each row represents a single record, while columns define specific attributes of that record. Tables are connected through relationships defined by primary keys (unique identifiers for each row) and foreign keys (references to primary keys in other tables).
For example, a customer relationship management (CRM) system might have separate tables for customers, orders, and products. The orders table would use foreign keys to reference both the customer who placed the order and the products included, creating relationships across the database that enable powerful SQL queries to retrieve and analyze relational data.
What are non relational databases?
Non-relational databases represent a diverse category of database systems designed to handle large-scale data storage and high-performance workloads that don't fit neatly into traditional table structures. These systems provide flexible schema designs that adapt to changing data requirements without extensive database restructuring.
NoSQL databases are good at managing big data, unstructured data, and semi-structured data that are difficult to model in table formats. They typically offer eventual consistency rather than ACID compliance, trading some data consistency guarantees for improved scalability and lower latency (the time between a query and a response from the database) in distributed environments.
How do non-relational databases store data?
There are several types of non-relational databases so how they store data depends on the data model, which is typically optimized for specific use cases:
Document databases like MongoDB store data as JSON or BSON documents, allowing each record to have its own structure. This document-oriented approach works well for content management systems and applications with evolving data structures.
Key-value stores such as Redis and DynamoDB organize data as simple key-value pairs, making them extremely fast for caching and session management. Each key maps directly to a value, enabling high-speed data retrieval.
Graph databases like Neo4j represent data as nodes connected by relationships, making them ideal for social networks, fraud detection, and recommendation engines where connections between data points matter as much as the data itself.
Column-family stores like Cassandra organize data into columns rather than rows, optimizing data processing for analytics and large datasets that require high availability across distributed systems.
Key differences: relational vs. non-relational databases
There are fundamental differences between relational and non-relational databases, but it's not uncommon for enterprises to use different database types for different use cases. Here are some key differences:
Data structure and schema: Relational databases require a predefined schema that specifies exactly how data should be organized before any information is stored. Non-relational databases offer flexible schema or schema-less designs that let you store different types of data without restructuring the entire database.
Query language: Relational database systems use structured query language (SQL) for complex queries that join multiple tables and filter data. NoSQL databases typically use API calls or database-specific query languages optimized for their particular data model.
Scalability: Relational databases traditionally scale vertically by adding more power to a single server, which has physical limits. Non-relational databases support horizontal scaling by distributing data across multiple nodes to handle large volumes of data and high-traffic workloads.
Data integrity vs. performance: Relational databases prioritize ACID compliance to ensure data consistency and data integrity, which is essential for financial systems and applications where accuracy is critical. Non-relational databases often use eventual consistency models that optimize for high performance and real-time data access at the cost of immediate consistency across all nodes.
Relationships and joins: The relational model is great for managing data relationships through foreign keys and complex queries that join information from multiple tables. Non-relational databases typically embed related data within documents or use denormalization, avoiding the need for joins, but sometimes duplicating data.
When to use relational vs. non-relational databases
Choose relational databases when you need:
Structured data with clear relationships and consistent formats
ACID compliance for financial transactions, inventory management, or applications requiring absolute data accuracy
Complex queries involving multiple tables and sophisticated filtering
Strong data validation and schema enforcement
Traditional business applications, including enterprise resource planning (ERP) or CRM platforms, often benefit from the data consistency and mature functionality that relational database systems provide. Many developers have established SQL expertise, which makes a RMDBS easy to adopt and maintain.
That said, developers aren’t the only consideration. Some platforms, like Airtable, are built on a relational database, but provide an intuitive, no-code AI interface that unlocks the power of a relational database for non-technical teams. This means that your product, project management, operations, finance, human resources, sales, and marketing teams, who are often without development resources, can streamline processes and build automated workflows on their own.
Choose non-relational databases when you need:
Flexible schema to accommodate rapidly changing data structures
Horizontal scaling to handle large-scale web applications or IoT data streams
High performance for real-time analytics, caching, or recommendation engines
Management of unstructured data like user-generated content, logs, or social media feeds
Distributed systems with high availability across geographic regions
Specific use cases and systems can perform better with the scalability and flexibility of NoSQL databases, so it’s important to understand your organization’s needs and goals.
How to choose between relational databases and non-relational databases
Whether you are a technical or non-technical team, it’s important to do the legwork required to understand how a database best serves your goals.
1. Begin by analyzing your data structure and the types of data you'll be managing.
If your information fits naturally into tables with consistent fields and clear relationships, a relational model likely makes sense. If you're handling diverse data types, rapidly evolving structures, or large amounts of data that don't follow predictable patterns, non-relational databases offer more flexibility.
2. Consider your scalability requirements and projected growth.
Massive data volumes and applications that may experience unpredictable traffic spikes benefit from the horizontal scaling that non-relational databases offer. But if you anticipate steady, predictable growth, vertical scaling with a traditional RDBMS may be more cost-effective.
3. Evaluate your consistency needs versus performance requirements.
Financial applications, e-commercetransactions, and systems requiring data durability should prioritize the ACID properties of relational databases. Applications where slightly outdated data is acceptable—such as social media feeds or caching layers—can leverage the speed advantages of eventual consistency in NoSQL systems.
4. Consider your team's expertise and the maturity of available tools.
SQL remains the industry standard, with decades of optimization and a vast ecosystem of open-source tools. As NoSQL technologies mature, they may require specialized knowledge for proper implementation and optimization.
Summary of differences: relational vs. non-relational databases
Feature
Relational Databases
Non-Relational Databases
Data model
Tables with rows and columns
Documents, key-value pairs, graphs, or column families
Schema
Fixed, predefined schema
Flexible schema or schema-less
Scaling
Vertical scaling (adding server resources)
Horizontal scaling (distributing across nodes through sharding)
Query language
SQL (structured query language)
Varies by database type (API calls, database-specific languages)
Consistency
ACID compliance (strong consistency)
Eventual consistency (varies by implementation)
Best for
Structured data, complex queries, data integrity
Unstructured data, high performance, large datasets
Examples
MySQL, PostgreSQL, Oracle, SQL Server
MongoDB, Cassandra, Redis, DynamoDB, CouchDB, Neo4j
Relationships
Foreign keys and joins across multiple tables
Embedded documents or denormalization optimization strategy
Cons of non-relational databases
Non-relational databases are a good match for specific use cases, but come with tradeoffs:
The lack of a standardized query language means that teams must learn database-specific syntax and approaches.
Eventual consistency can also create challenges for applications that require immediate data accuracy.
Non-relational databases may struggle with complex queries that would be straightforward in SQL, particularly those requiring joins across multiple data collections.
The flexible schema that makes NoSQL databases adaptable can also lead to data management challenges when different programming languages or teams use inconsistent data structures.
While NoSQL databases excel at horizontal scaling, optimizing performance for large-scale deployments requires specialized expertise in areas like sharding strategies, node configuration, and caching mechanisms that aren't necessarily as well-documented as traditional RDBMS optimization techniques.
See the power of a relational database today
The choice between relational and non-relational databases isn’t necessarily binary. Modern data platforms can combine the strengths of both approaches, providing the structure and data integrity of relational models with the flexibility and scalability of NoSQL systems.
Airtable's AI-native platform combines the functionality of relational databases with an intuitive no-code interface that makes data management accessible across your internal teams, offering multiple views of the same underlying data. Whether teams are managing product roadmaps or customer feedback, tracking projects or marketing campaigns, or analyzing business data, Airtable enables your teams to create relationships between tables, maintain data consistency, and scale as your needs grow—all without database administration expertise.
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